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Automatic Segmentation of Neonatal Brain MRI [chapter]

Marcel Prastawa, John Gilmore, Weili Lin, Guido Gerig
2004 Lecture Notes in Computer Science  
The method first uses graph clustering and robust estimation to estimate the initial intensity distributions. The estimates are then used together with the spatial priors to perform bias correction.  ...  This paper describes an automatic tissue segmentation method for neonatal MRI.  ...  dataset, Koen Van Leemput for providing the MATLAB code that aids the development of the bias correction software, and the ITK community ( for providing the software framework for the segmentation  ... 
doi:10.1007/978-3-540-30135-6_2 fatcat:eb2kipghnrbsfin4fonp5eoqla

Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentation [chapter]

M. Freiman, A. Kronman, S. J. Esses, L. Joskowicz, J. Sosna
2010 Lecture Notes in Computer Science  
The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field.  ...  We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images.  ...  However, extensive user interaction is required to provide estimates of the prior intensity models, and to prevent "segmentation leaks" with predefined spatial constraints.  ... 
doi:10.1007/978-3-642-15711-0_10 fatcat:ihfll6y3jzdabk6fo3xwcznsne

ShapeCut: Bayesian surface estimation using shape-driven graph

Gopalkrishna Veni, Shireen Y. Elhabian, Ross T. Whitaker
2017 Medical Image Analysis  
The strategy of estimating optimal segmentations within a statistical framework that combines image data with priors on anatomical structures promises to address some of these technical challenges.  ...  A variety of medical image segmentation problems present significant technical challenges, including heterogeneous pixel intensities, noisy/ill-defined boundaries and irregular shapes with high variability  ...  The authors would like to thank the Comprehensive Arrhythmia Research and Management (CARMA) Center at the University of Utah for providing LGE-MRI left atrium scans, with a special thanks to Joshua Cates  ... 
doi:10.1016/ pmid:28582702 pmcid:PMC5546629 fatcat:pccmvszcureehoia4jxeo5wz5i

Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors [chapter]

Adrian Vasile Dalca, Ramesh Sridharan, Lisa Cloonan, Kaitlin M. Fitzpatrick, Allison Kanakis, Karen L. Furie, Jonathan Rosand, Ona Wu, Mert Sabuncu, Natalia S. Rost, Polina Golland
2014 Lecture Notes in Computer Science  
We propose and demonstrate an inference algorithm for the automatic segmentation of cerebrovascular pathologies in clinical MR images of the brain.  ...  Unlike normal brain tissues and structures, the location and shape of the lesions vary across patients, presenting serious challenges for prior-driven segmentation.  ...  Right: Volume estimates based on the automatic segmentation of leukoaraiosis against volume estimates based on the manual segmentations; the correlation coefficient is r 0.82.  ... 
doi:10.1007/978-3-319-10470-6_96 fatcat:d7jayrvn7ncuvgegfg62qcnx7a

Automatic segmentation of MR images of the developing newborn brain

Marcel Prastawa, John H. Gilmore, Weili Lin, Guido Gerig
2005 Medical Image Analysis  
The distribution estimates are then used together with the spatial priors to perform bias correction.  ...  This paper describes an automatic tissue segmentation method for newborn brains from magnetic resonance images (MRI).  ...  Therefore, it is necessary to use automatic segmentation methods for clinical studies with multiple subjects.  ... 
doi:10.1016/ pmid:16019252 fatcat:il3nak6j6bfndmae3rkfu2dmem

Automatic brain tumor segmentation by subject specific modification of atlas priors1

Marcel Prastawa, Elizabeth Bullitt, Nathan Moon, Koen Van Leemput, Guido Gerig
2003 Academic Radiology  
The results obtained from the automatic segmentation program are compared with results from manual and semi-automated methods.  ...  This atlas is modified with the subject-specific brain tumor prior that is computed based on contrast enhancement. Results. Five cases with different types of tumors are selected for evaluation.  ...  The semi-automatic segmentations are generated from the T1 contrast difference image with the user specifying the rough estimate of the tumor location by placing bubbles in the 3D volume.  ... 
doi:10.1016/s1076-6332(03)00506-3 pmid:14697002 pmcid:PMC2430604 fatcat:tayzonl4pff4znrpmg6wzkriza

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Anne-Sophie Dewalle-Vignion, Nacim Betrouni, Clio Baillet, Maximilien Vermandel
2015 Physics in Medicine and Biology  
Conclusions: The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in  ...  In this context, the Simultaneous Truth and Performance Level Estimation algorithm could be useful to manage the multi-observers variability.  ...  The STAPLE algorithm computes a probabilistic estimate of the ground truth from a set of (automatic or manual) segmentations results.  ... 
doi:10.1088/0031-9155/60/24/9473 pmid:26584044 fatcat:ewr5byjwpnewpoc7qej33b4m4m

A novel probabilistic simultaneous segmentation and registration using level set

Melih S. Aslan, Eslam Mostafa, Hossam Abdelmunim, Ahmed Shalaby, Aly A. Farag, Ben Arnold
2011 2011 18th IEEE International Conference on Image Processing  
We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior.  ...  The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images.  ...  Fig. 2 . 2 Obtaining the shape prior image. Training CT slices of different data sets. The last column shows the shape prior image with variability region.  ... 
doi:10.1109/icip.2011.6116039 dblp:conf/icip/AslanMASFA11 fatcat:2y2uyt5kd5c7hfxcdbdasygzwm

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients [article]

Zhen Yuan, Esther Puyol-Anton, Haran Jogeesvaran, Catriona Reid, Baba Inusa, Andrew P. King
2020 arXiv   pre-print
We investigate the use of deep learning to perform automatic estimation of spleen length from ultrasound images.  ...  Our best model (segmentation-based) achieved a percentage length error of 7.42%, which is approaching the level of inter-observer variability (5.47%-6.34%).  ...  All patients were children with SCD and received professional clinical consultation prior to ultrasound inspection.  ... 
arXiv:2009.02704v1 fatcat:mo5tfsirejff3fo276wnhvp744

3D Vertebral Body Segmentation Using Shape Based Graph Cuts

Melih S. Aslan, Asem Ali, Aly A. Farag, Ham Rara, Ben Arnold, Ping Xiang
2010 2010 20th International Conference on Pattern Recognition  
To segment a VB, the Matched filter is used to detect the VB region automatically. We align the detected volume with 3D shape prior in order to be used in distance probabilistic model.  ...  Then, we estimate the shape variations using a distance probabilistic model which approximates the marginal densities of the VB and background in the variability region.  ...  Obtaining the shape prior volume: (a)-(c) Some training CT slices from different data sets. (d) The shape prior slice with variability region. Figure 4 . 4 Views of the 3D shape prior.  ... 
doi:10.1109/icpr.2010.961 dblp:conf/icpr/AslanAFRAX10 fatcat:xf36x4724rgulinmekxkujdyte

Coupled nonparametric shape priors for segmentation of multiple basal ganglia structures

Gokhan Uzunbas, Mujdat Cetin, Gozde Unal, Aytul Ercil
2008 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel  ...  density estimation of multiple shapes.  ...  The authors would like to thank Junmo Kim for providing his code for nonparametric shape prior. The authors would also like to thank Dr.  ... 
doi:10.1109/isbi.2008.4540971 dblp:conf/isbi/UzunbasCUE08 fatcat:4tw6cinwdrec5l7lgjqg2zo6ya

Estimation of total intracranial volume; a comparison of methods

Gerard Ridgway, Josephine Barnes, Tracey Pepple, Nick Fox
2011 Alzheimer's & Dementia  
Results Considering baseline TIV, SPM5 segmentations are highly variable and upwardly biased with respect to manual measures, while those from SPM8 are dramatically improved; FreeSurfer results lie between  ...  or modulated warped segmentations; SPM version 8 equivalents, which use the improved tissue prior probability maps shown in Figure 1 [4] ; and Jacobian integration using either SPM8 unified segmentation  ... 
doi:10.1016/j.jalz.2011.05.099 fatcat:7mxq4munpbfwhf3qjp43ifrpze

Automatic segmentation of newborn brain MRI

Neil I. Weisenfeld, Simon K. Warfield
2009 NeuroImage  
Because manual interaction is time consuming and introduces both bias and variability, we have developed a novel automatic segmentation algorithm for brain MRI of newborn infants.  ...  We compared the performance of our algorithm with a previously published semi-automated algorithm and with expert-drawn images.  ...  Hoai-Huong Tran for drawing manual segmentations.  ... 
doi:10.1016/j.neuroimage.2009.04.068 pmid:19409502 pmcid:PMC2945911 fatcat:74ndovbvijawlhtw5eafrg5qdu

Adaptive image segmentation for robust measurement of longitudinal brain tissue change

E. Fletcher, B. Singh, D. Harvey, O. Carmichael, C. DeCarli
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
These results suggest that this method may be useful for robust longitudinal brain tissue change estimation.  ...  We present a method that significantly improves magnetic resonance imaging (MRI) based brain tissue segmentation by modeling the topography of boundaries between tissue compartments.  ...  Fig. 4 shows samples of segmentation of the BrainWeb template with 3% noise, using the adaptive prior (equations (5-6)) compared with a prior that favors homogeneous tissue neighborhoods everywhere [  ... 
doi:10.1109/embc.2012.6347195 pmid:23367130 pmcid:PMC3776590 fatcat:h3f6ylylargnfcteolbwcprlga

Fully automatic extraction of salient objects from videos in near real-time [article]

Akamine Kazuma, Ken Fukuchi, Akisato Kimura, Shigeru Takagi
2010 arXiv   pre-print
To this end, we propose a new and quite fast method for automatic video segmentation with the help of 1) efficient optimization of Markov random fields with polynomial time of number of pixels by introducing  ...  graph cuts, 2) automatic, computationally efficient but stable derivation of segmentation priors using visual saliency and sequential update mechanism, and 3) an implementation strategy in the principle  ...  Our main contributions are as follows: 1) We newly incorporate saliency-based priors into frame-wise segmentation with graph cuts to achieve fully automatic segmentation.  ... 
arXiv:1008.0502v2 fatcat:w55b4hjyo5ed3kjsu757skegdu
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